Automatic Paddy Leaf Disease Detection Based on GLCM Using Multiclass Support Vector Machine
The paddy leaf diseases have increased rapidly in the recent years because of globalization, environmental pollution and climate changes which reduce the production of rice and economy of the country. For healthy growth of rice plants there is a need of automatic system which can detect the paddy diseases automatically on time to give the proper treatment for the affected plants. In this paper, we proposed a methodology to develop an automatic system for detect the paddy disease which are Paddy Blast Disease, Brown Spot Disease, Narrow Brown Spot Disease using MATLAB. This paper concentrate on the image processing techniques used to enhance the quality of the image and Multiclass Support Vector Machine to classify the paddy diseases. The methodology involves image acquisition, pre-processing, segmentation, feature extraction and classification of the paddy diseases. Image segmentation technique is used to detect infected parts of leaf by using canny edge detection, multilevel thresholding and region growing techniques. We extract texture features using GLCM (grey level co- occurrence matrix) techniques, additionally we extract color and shape features to improve the accuracy of the framework and use Multiclass Support Vector Machine for classification. We achieved 87.5% accuracy for the test dataset.
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